Adaptive fault-tolerant control for switched nonlinear systems based on command filter technique

This article put forward an adaptive neural fault-tolerant control strategy for a class of switched nonlinear systems subject to actuator fault by means of the command filter approach. By using neural networks, the unknown nonlinear functions of the system under consideration are approximated, while its unmeasurable states are estimated by establishing a switched observer. Furthermore, the explosion of complexity issue, which arises from the derivatives of virtual controllers, is addressed with the command filter method. In order to reduce filter errors and overcome the drawbacks of the most traditional approaches, such as the ones based on dynamic surface control techniques, an error compensation mechanism was developed. In summary, by taking advantage of the command filter approach, backstepping algorithm, and average dwell time method, an adaptive NNs fault tolerant controller is established for the system under consideration. Finally, the designed controller has the ability to make the reference signal which can be tracked by the output of the system as close as possible and the boundness of all signals within the closed-loop system can be guaranteed. The usefulness of the designed controller is illustrated by the simulation example. (c) 2020 Elsevier Inc. All rights reserved.

Automated summary

An adaptive neural fault-tolerant control strategy for a class of switched nonlinear systems subject to actuator fault is proposed in this article, using the command filter approach to approximate unknown nonlinear functions and estimate unmeasurable states. By applying the backstepping algorithm and average dwell time method, an adaptive NNs fault tolerant controller is developed to ensure signal boundedness within the system.